Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Detection of API(Anomaly Process Instance) Based on Distance for Process Mining

프로세스 마이닝을 위한 거리 기반의 API(Anomaly Process Instance) 탐지법

  • Jeon, Daeuk (Department of Industrial Engineering, Pusan National University) ;
  • Bae, Hyerim (Department of Industrial Engineering, Pusan National University)
  • Received : 2015.03.20
  • Accepted : 2015.09.30
  • Published : 2015.12.15
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Abstract

There have been many attempts to find knowledge from data using conventional statistics, data mining, artificial intelligence, machine learning and pattern recognition. In those research areas, knowledge is approached in two ways. Firstly, researchers discover knowledge represented in general features for universal recognition, and secondly, they discover exceptional and distinctive features. In process mining, an instance is sequential information bounded by case ID, known as process instance. Here, an exceptional process instance can cause a problem in the analysis and discovery algorithm. Hence, in this paper we develop a method to detect the knowledge of exceptional and distinctive features when performing process mining. We propose a method for anomaly detection named Distance-based Anomaly Process Instance Detection (DAPID) which utilizes distance between process instances. DAPID contributes to a discovery of distinctive characteristic of process instance. For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. Additionally, we experiment on real data from a domestic port terminal to demonstrate our proposed methodology.

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References

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